Using organoclay to promote morphology refinement and co-continuity in high-density polyethylene/polyamide 6 blends - Effect of filler content and polymer matrix composition

We investigate the gradual changes of the microstructure of two blends of high-density polyethylene (HDPE) and polyamide 6 (PA6) at opposite composition filled with increasing amounts of an organomodified clay. The filler locates preferentially inside the polyamide phase, bringing about radical alterations in the micron-scale arrangement of the polymer phases. When the host polyamide represents the major constituent, a sudden reduction of the average sizes of the polyethylene droplets was observed upon addition of even low amounts of organoclay. A morphology refinement was also noticed at low filler contents when the particles distributes inside the minor phase. In this case, however, keep increasing the organoclay content eventually results in a high degree of PA6 phase continuity. Rheological analyses reveal that the filler loading at which the polyamide assembles in a continuous network corresponds to the critical threshold for its rheological transition from a liquid- to a gel-like behaviour, which is indicative of the structuring of the filler inside the host PA6. On the basis of this finding, a schematic mechanism is proposed in which the role of the filler in driving the space arrangement of the polymer phases is discussed. Finally, we show that the synergism between the reinforcing action of the filler and its ability to affect the blend microstructure can be exploited in order to enhance relevant technological properties of the materials, such as their high temperature structural integrity.

Functional astrocyte-neuron lactate shuttle in a human stem cell-derived neuronal network

The NT2.D1 cell line is one of the most well-documented embryocarcinoma cell lines, and can be differentiated into neurons and astrocytes. Great focus has also been placed on defining the electrophysiological properties of the neuronal cells, and more recently we have investigated the functional properties of their associated astrocytes. We now show for the first time that human stem cell-derived astrocytes produce glycogen and that co-cultures of these cells demonstrate a functional astrocyte-neuron lactate shuttle (ANLS). The ANLS hypothesis proposes that during neuronal activity, glutamate released into the synaptic cleft is taken up by astrocytes and triggers glucose uptake, which is converted into lactate and released via monocarboxylate transporters for neuronal use. Using mixed cultures of NT2-derived neurons and astrocytes, we have shown that these cells modulate their glucose uptake in response to glutamate. Additionally, we demonstrate that in response to increased neuronal activity and under hypoglycaemic conditions, co-cultures modulate glycogen turnover and increase lactate production. Similar results were also shown after treatment with glutamate, potassium, isoproterenol, and dbcAMP. Together, these results demonstrate for the first time a functional ANLS in a human stem cell-derived co-culture. © 2013 ISCBFM.

Deschooling society? A lifelong learning network for sustainable communities, urban regeneration and environmental technologies

The complexity and multifaceted nature of sustainable lifelong learning can be effectively addressed by a broad network of providers working co-operatively and collaboratively. Such a network involving the third, public and private sector bodies must realise the full potential of accredited flexible and blended formal learning, contextual opportunities offered by enablers of informal and non formal learning and the affordances derived from the various loose and open spaces that can make social learning effective. Such a conception informs the new Lifelong Learning Network Consortium on Sustainable Communities, Urban Regeneration and Environmental Technologies established and led by the Lifelong Learning Centre at Aston University. This paper offers a radical, reflective and political evaluation of its first year in development arguing that networked learning of this type could prefigure a new model for lifelong learning and sustainable education that renders the city itself a creative medium for transformative learning and sustainability.

Aβ(1-42) induced metabolic effects in a stem cell derived neuron and astrocyte network.

Alzheimer’s Disease (AD) is the most common form of dementia currently affecting more than 35 million people worldwide. Hypometabolism is a major feature of AD and appears decades before cognitive decline and pathological lesions. This has a detrimental impact on the brain which has a high energy demand. Current models of AD fail to mimic all the features of the disease, which has an impact on the development of new therapies. Human stem cell derived models of the brain have attracted a lot of attention in recent years as a tool to study neurodegenerative diseases. In this thesis, neurons and astrocytes derived from the human embryonal carcinoma cell line (NT2/D1) were utilised to determine the metabolic coupling between neurons and astrocytes with regards to responses to hypoglycaemia, neuromodulators and increase in neuronal activity. This model was then used to investigate the effects of Aß(1-42) on the metabolism of these NT2-derived co-cultures as well as pure astrocytes. Additionally primary cortical mixed neuronal and glial cultures were utilised to compare this model to a widely accepted in vitro model used in Alzheimer’s disease research. Co-cultures were found to respond to Aß(1-42) in similar way to human and in vivo models. Hypometabolism was characterised by changes in glucose metabolism, as well as lactate, pyruvate and glycogen. This led to a significant decrease in ATP and the ratio of NAD+/NADH. These results together with an increase in calcium oscillations and a decrease in GSH/GSSG ratio, suggests Aß-induces metabolic and oxidative stress. This situation could have detrimental effects in the brain which has a high energy demand, especially in terms of memory formation and antioxidant capacity.

Intra-channel nonlinearity compensation for PM-16QAM traffic co-propagating with 28Gbaud m-ary QAM neighbours

We quantify the benefits of intra-channel nonlinear compensation in meshed optical networks, in view of network configuration, fibre design aspect, and dispersion management. We report that for a WDM optical transport network employing flexible 28Gbaud PM-mQAM transponders with no in-line dispersion compensation, intrachannel nonlinear compensation, for PM-16QAM through traffic, offers significant improvements of up to 4dB in nonlinear tolerance (Q-factor) irrespective of the co-propagating modulation format, and that this benefit is further enhanced (1.5dB) by increasing local link dispersion. For dispersion managed links, we further report that advantages of intra-channel nonlinear compensation increase with in-line dispersion compensation ratio, with 1.5dB improvements after 95% in-line dispersion compensation, compared to uncompensated transmission. © 2012 Optical Society of America.

Managing supply networks:organizational roles in network management

This paper describes research findings on the roles that organizations can adopt in managing supply networks. Drawing on extensive empirical data, it is demonstrated that organizations may be said to be able to manage supply networks, provided a broad view of ‘managing’ is adopted. Applying role theory, supply network management interventions were clustered into sets of linked activities and goals that constituted supply network management roles. Six supply network management roles were identified – innovation facilitator, co-ordinator, supply policy maker and implementer, advisor, information broker and supply network structuring agent. The findings are positioned in the wider context of debates about the meaning of management, the contribution of role theory to our understanding of management, and whether inter-organizational networks can be managed.

Supply network strategy:role and competence requirements

This paper presents an argument that it is possible for an organisation to manage networks, but understanding this involves consideration of what is meant by "managing". Based on prior research and data from a major longitudinal action research study in the health sector, the paper describes six network management roles: network structuring agent; co-ordinator; advisor; information broker; relationship broker; innovation sponsor. The necessary "assets" for effective performance of these roles are identified, in particular those relating to team competence. The findings enrich and significantly develop previous work on network management roles and activities, and their influencing factors. It is concluded that, given the specific nature of the networks studied, further research is required to evaluate the generalisability of the findings, though initial indications are promising.

Reactive processing of polymers:functionalisation of ethylene-propylene diene terpolymer (EPDM) in the presence and absence of a co-agent and effect of functionalised EPDM on compatibilisation of poly(ethylene terephthalate)/EPDM blends

Ethylene-propylene diene terpolymer (EPDM) was functionalized with glycidyl methacrylate (GMA) during melt processing by free radical grafting with peroxide initiation in the presence and absence of a reactive comonomer trimethylolpropane triacrylate (Tris). Increasing the peroxide concentration resulted in an increase in the GMA grafting yield, albeit the overall grafting level was low and was accompanied by higher degree of crosslinking of EPDM which was found to be the major competing reaction. The presence of Tris in the grafting system gave rise to higher grafting yield produced at a much lower peroxide concentration though the crosslinking reactions remained high but without the formation of GMA-homopolymer in either of the two systems. The use of these functionalized EPDM (f-EPDM) samples with PET as compatibilisers in binary and ternary blends of PET/EPDM/f-EPDM was evaluated. The influence of the different functionalisation routes of the rubber phase (in presence and absence of Tris) and the effect of the level of functionality and microstructure of the resultant f-EPDM on the extent of the interfacial reaction, morphology and mechanical properties was also investigated. It is suggested that the mechanical properties of the blends are strongly influenced by the performance of the graft copolymer, which is in turn, determined by the level of functionality, molecular structure of the functionalized rubber and the interfacial concentration of the graft copolymer across the interface. The cumulative evidence obtained from torque rheometry, scanning electron microscopy, SEM, dynamic mechanical analysis (DMA), tensile mechanical tests and Fourier transform infrared (FTIR) supports this. It was shown that binary and ternary blends prepared with f-EPDM in the absence of Tris and containing lower levels of g-GMA effected a significant improvement in mechanical properties. This increase, particularly in elongation to break, could be accounted for by the occurrence of a reaction between the epoxy groups of GMA and the hydroxyl/carboxyl end groups of PET that would result in a graft copolymer which could, most probably, preferentially locate at the interface, thereby acting as an 'emulsifier' responsible for decreasing the interfacial tension between the otherwise two immiscible phases. This is supported by results from FTIR analysis of the fractionated PET phase of these blends which confirm the formation of an interfacial reaction, DMA results which show a clear shift in the T s of the blend components and SEM results which reveal very fine morphology, suggesting effective compatibilisation that is concomitant with the improvement observed in their tensile properties. Although Tris has given rise to highest amount of g-GMA, it resulted in lower mechanical properties than the optimized blends produced in the absence of Tris. This was attributed to the difference in the microstructure of the graft and the level of functionality in these samples resulting in less favourable structure responsible for the coarser dispersion of the rubber phase observed by SEM, the lower extent of T shift of the PET phase (DMA), the lower height of the torque curve during reactive blending and FTIR analysis of the separated PET phase that has indicated a lower extent of the interfacial chemical reaction between the phases in this Tris-containing blend sample. © 2005 Elsevier Ltd. All rights reserved.

A comparison of two distributed large-volume measurement systems:the mobile spatial co-ordinate measuring system and the indoor global positioning system

Advances in the area of industrial metrology have generated new technologies that are capable of measuring components with complex geometry and large dimensions. However, no standard or best-practice guides are available for the majority of such systems. Therefore, these new systems require appropriate testing and verification in order for the users to understand their full potential prior to their deployment in a real manufacturing environment. This is a crucial stage, especially when more than one system can be used for a specific measurement task. In this paper, two relatively new large-volume measurement systems, the mobile spatial co-ordinate measuring system (MScMS) and the indoor global positioning system (iGPS), are reviewed. These two systems utilize different technologies: the MScMS is based on ultrasound and radiofrequency signal transmission and the iGPS uses laser technology. Both systems have components with small dimensions that are distributed around the measuring area to form a network of sensors allowing rapid dimensional measurements to be performed in relation to large-size objects, with typical dimensions of several decametres. The portability, reconfigurability, and ease of installation make these systems attractive for many industries that manufacture large-scale products. In this paper, the major technical aspects of the two systems are briefly described and compared. Initial results of the tests performed to establish the repeatability and reproducibility of these systems are also presented. © IMechE 2009.

Functional astrocyte-neuron lactate shuttle in a human stem cell derived neuronal network

The development of stem cell-derived neuronal networks will promote experimental system development for drug screening, toxicological testing and disease modelling, providing that they mirror closely the functional competencies of their in vivo counterparts. The NT2 cell line is one of the best documented embryocarcinoma cell lines, and can be differentiated into neurons and astrocytes. Great focus has also been placed on defining the electrophysiological properties of these cells, and more recently we have investigated the functional properties of their associated astrocytes. We now show for the first time in a human stem cell derived co-culture model that these cultures are also metabolically competent and demonstrate a functional astrocyte neuron lactate shuttle (ANLS). The ANLS hypothesis proposes that during neuronal activity, glutamate released into the synaptic cleft is taken up by astrocytes and triggers glucose uptake which is converted into lactate and released via monocarboxylate transporters for neuronal use. Using mixed cultures of NT2 derived neurons and astrocytes we have shown that these cells modulate their glucose uptake in response to glutamate, an effect that was blocked by cytochalasin B and ouabain. Additionally we demonstrate that in response to increased neuronal activity and under hypoglycaemic conditions, co-cultures modulate glycogen turnover and increase lactate production. Similar results were also shown following treatment with glutamate, potassium, Isoproterenol and dbcAMP. Together these results demonstrate for the first time a functional ANLS in a human stem cell derived co-culture.

Numerical analysis of artificial neural network and volterra-based nonlinear equalizers for coherent optical OFDM

One major drawback of coherent optical orthogonal frequency-division multiplexing (CO-OFDM) that hitherto remains unsolved is its vulnerability to nonlinear fiber effects due to its high peak-to-average power ratio. Several digital signal processing techniques have been investigated for the compensation of fiber nonlinearities, e.g., digital back-propagation, nonlinear pre- and post-compensation and nonlinear equalizers (NLEs) based on the inverse Volterra-series transfer function (IVSTF). Alternatively, nonlinearities can be mitigated using nonlinear decision classifiers such as artificial neural networks (ANNs) based on a multilayer perceptron. In this paper, ANN-NLE is presented for a 16QAM CO-OFDM system. The capability of the proposed approach to compensate the fiber nonlinearities is numerically demonstrated for up to 100-Gb/s and over 1000km and compared to the benchmark IVSTF-NLE. Results show that in terms of Q-factor, for 100-Gb/s at 1000km of transmission, ANN-NLE outperforms linear equalization and IVSTF-NLE by 3.2dB and 1dB, respectively.

The network structure of Hungarian business clusters

This article investigates the attitudes to inter-firm co-operation in Hungary by analysing a special group of business networks: the business clusters. Following an overview of cluster policy, a wide range of selfproclaimed business clusters are identified. A small elite of these business networks evolves into successful, sustainable innovative business clusters. However, in the majority of cases, these consortia of interfirm co-operation are not based on a mutually satisfactory model, and as a consequence, many clusters do not survive in the longer term. The paper uses the concepts and models of social network theory in order to explain, why and under what circumstances inter-firm co-operation in clusters enhances the competitiveness of the network as a whole, or alternatively, under what circumstances the cluster remains dependent on Government subsidies. The empirical basis of the study is a thorough internet research about the Hungarian cluster movement; a questionnaire based expert survey among managers of clusters and member companies and a set of in-depth interviews among managers of self-proclaimed clusters. The last chapter analyises the applicability of social network theory in the analysis of business networks and a model involving the value chain is recommended.

Design strategies for an artificial neural network based algorithm for automatic incident detection on major arterial streets

Traffic incidents are non-recurring events that can cause a temporary reduction in roadway capacity. They have been recognized as a major contributor to traffic congestion on our nation’s highway systems. To alleviate their impacts on capacity, automatic incident detection (AID) has been applied as an incident management strategy to reduce the total incident duration. AID relies on an algorithm to identify the occurrence of incidents by analyzing real-time traffic data collected from surveillance detectors. Significant research has been performed to develop AID algorithms for incident detection on freeways; however, similar research on major arterial streets remains largely at the initial stage of development and testing. This dissertation research aims to identify design strategies for the deployment of an Artificial Neural Network (ANN) based AID algorithm for major arterial streets. A section of the US-1 corridor in Miami-Dade County, Florida was coded in the CORSIM microscopic simulation model to generate data for both model calibration and validation. To better capture the relationship between the traffic data and the corresponding incident status, Discrete Wavelet Transform (DWT) and data normalization were applied to the simulated data. Multiple ANN models were then developed for different detector configurations, historical data usage, and the selection of traffic flow parameters. To assess the performance of different design alternatives, the model outputs were compared based on both detection rate (DR) and false alarm rate (FAR). The results show that the best models were able to achieve a high DR of between 90% and 95%, a mean time to detect (MTTD) of 55-85 seconds, and a FAR below 4%. The results also show that a detector configuration including only the mid-block and upstream detectors performs almost as well as one that also includes a downstream detector. In addition, DWT was found to be able to improve model performance, and the use of historical data from previous time cycles improved the detection rate. Speed was found to have the most significant impact on the detection rate, while volume was found to contribute the least. The results from this research provide useful insights on the design of AID for arterial street applications.

Field programmable gate array based target detection and gesture recognition

The move from Standard Definition (SD) to High Definition (HD) represents a six times increases in data, which needs to be processed. With expanding resolutions and evolving compression, there is a need for high performance with flexible architectures to allow for quick upgrade ability. The technology advances in image display resolutions, advanced compression techniques, and video intelligence. Software implementation of these systems can attain accuracy with tradeoffs among processing performance (to achieve specified frame rates, working on large image data sets), power and cost constraints. There is a need for new architectures to be in pace with the fast innovations in video and imaging. It contains dedicated hardware implementation of the pixel and frame rate processes on Field Programmable Gate Array (FPGA) to achieve the real-time performance. ^ The following outlines the contributions of the dissertation. (1) We develop a target detection system by applying a novel running average mean threshold (RAMT) approach to globalize the threshold required for background subtraction. This approach adapts the threshold automatically to different environments (indoor and outdoor) and different targets (humans and vehicles). For low power consumption and better performance, we design the complete system on FPGA. (2) We introduce a safe distance factor and develop an algorithm for occlusion occurrence detection during target tracking. A novel mean-threshold is calculated by motion-position analysis. (3) A new strategy for gesture recognition is developed using Combinational Neural Networks (CNN) based on a tree structure. Analysis of the method is done on American Sign Language (ASL) gestures. We introduce novel point of interests approach to reduce the feature vector size and gradient threshold approach for accurate classification. (4) We design a gesture recognition system using a hardware/ software co-simulation neural network for high speed and low memory storage requirements provided by the FPGA. We develop an innovative maximum distant algorithm which uses only 0.39% of the image as the feature vector to train and test the system design. Database set gestures involved in different applications may vary. Therefore, it is highly essential to keep the feature vector as low as possible while maintaining the same accuracy and performance^

Mining the online social network data: Influence, summarization, and organization

Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it. This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign? The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.